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A Nordic Chapter under Society of Risk Analysis

SRA-E: NORDIC is a newly established regional chapter of the Society for Risk Analysis Europe (www.sraeurope.org). The Nordic chapter of SRA Europe is meant to be a node for networking between risk researchers and between risk research and policy makers and other decision makers in the Nordic and the Baltic countries.

The Nordic chapter shares the aim with SRA Europe which is

“to bring together individuals and organisations interested in risk assessment, risk management and risk communication in Europe”.

More specifically the SRA-E: NORDIC will promote risk research and knowledge and understanding of risk analysis techniques. This can be to identify and address specifically Nordic and Baltic issues in the field of risk, to promote debate, and facilitate exchanges of information and opinion between professionals in industry, government, universities, research institutes, and consultancies. The chapter has the ambition to convene and promote scientific and educational meetings on risk research, risk analysis and risk management in the Nordic and Baltic countries.

The Nordic Chapter welcomes a broad range of risk research from different disciplines.

Information about activities under the SRA-E: NORDIC will be distributed through an e-mail list. Subscribe here.

As a first event under SRA-E: NORDIC a risk conference will be held in Lund, Sweden, November 16-17, 2015. Further information on the conference can be found at www.lucram.lu.se/event/nordic-chapter-2015-risk-conference.

We invite suggestions for activities under SRA-E: NORDIC to be discussed on November 17, 2015 in Lund. Then we will also elect the Board of the Nordic Chapter.

 

June 18, 2015

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Adaptation – novel decision problems under uncertainty seeking solutions with scientific rigor

Attending the 2nd European Climate Change Adaptation Conference ECCA 12-14 May 2015, Copenhagen, Denmark.

Let me share some insights after the first day:

Adaptation is just waiting for innovative solutions designed with scientific methods and scientific rigor.

Adaption means that we have to face problems with high complexity and uncertainty which makes our knowledge far from precise.

Adaptation does not have to be something negative. See adaptation as innovation opportunities.

Adaptation calls for new concepts (like climate services) and methodologies (like climate adaptation monitoring and evaluation systems).

Adaptation is on the research agenda – and it requires a different way to use research.

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Picture taken at a session the first day of ECCA 2015 by Carin Nilsson

 

Day two of ECCA began with Richard Betts quoting Niels Bohr who said that

“It’s hard to make predictions – especially about the future”

True indeed, but is there anything new about making predictions to support adaptation compared to other predictions tasks? My question was partly answered throughout the day.

“Climate adaptation science” is indeed an area in the interface of research and policy.

The challenge of climate adaptation open up for a market of decision support tools. There is a high demand of information and evaluation support from policy and decision makers.

Even though existing principles for risk assessment and decision making adopted, there is still plenty room for development. Perhaps Climate Adaptation need novel ways to evaluate effectiveness and efficiency of adaptation strategies since the problems face deep uncertainty?

I heard several speakers talking about finding the optimal adaptation strategy or maximising efficiency. Can we really have the aim to optimise when knowledge is unreliable and uncertainty deep?

I found that many agreed on that we should seek solutions robust to uncertainty. There were even decision support tools that take deep uncertainty into account. To this I would like to add that we should also treat uncertainty in a way that is adequate with respect to the epistemic situation in knowledge that is available. That is what my ecca_poster was about.

Now I am going to let the ideas sink in and mature –  being very satisfied going to ECCA 2015.

~Ullrika

 

 

May 12, 2015

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Confronting uncertainties beyond Bayesianism

Confronting uncertainty beyond Bayesianism

is an initiative within the Advanced Study Group “Values, decision making and risk – current perspectives and future research” at the Pufendorf Institute in Lund, spring 2015.

The starting point of the theme is view of knowledge-based probability as the first order measure of uncertainty. The Bayesian approach to interpret and assign probabilities makes it possible to treat knowledge-based uncertainty in both parameters and model structure. The Bayesian approach rests upon coherent principles of logic reasoning and inference which easily transfers into decision analysis. It is put forward as the basic approach to assess and communicate knowledge-based uncertainty in risk. In strict Bayesian decision frameworks all underlying knowledge is taken care of by the values and knowledge-based probabilities.

There are at least two challenges to Bayesianism.

First, the knowledge basis to assign probabilities can be more or less strong while the Bayesian framework says nothing about how firmly the decision maker holds her degree of belief. However, representing all underlying knowledge (or information) by a precise probability (unique measure) is neither intuitive nor logically appropriate. This has led to development of alternative ways to represent uncertainty e.g. by expanding the probability measure into imprecise probabilities. We explore how these measures can be used in replacement of, or in combination, with Bayesian probabilities.

Second, parameters and model structure in a risk analysis is just one part of a knowledge production process. The use of any model has been preceded by problem framing and assessors choice of how to limit the problem and what sources of information to use.

The growing demand for science-informed policies to handle climate change and environmental problems has led to an increased attention to the treatment of knowledge-based uncertainty and the establishment of evidence bases for management. Lack of scientific knowledge is distinguished as important for the science policy interface, but how to manage lack of knowledge is more than just about asking for more knowledge.

Instead, qualitative aspects of knowledge provide an additional dimension to risk. The entire knowledge production process might reveal more types and sources of uncertainty to consider. It is relevant to ask if and how new types of uncertainty may affect the rationale and applicability of the Bayesian approach and it can be extended to confront unreliable knowledge-based probabilities.

Somehow the uncertainty of underlying knowledge should have an impact on how a decision analysis is set up and performed. What is a comprehensive quantitative decision theory that encompasses the affixing of probabilities in accordance with quality dimension of knowledge? What kinds of rules are required to adequately guide the choice of action to perform based on the strength of knowledge and other contextual factors?

We are not interested in a list of factors that induce uncertainty into modeling and decision making. Rather we seek to discuss how these factors can be incorporated in a comprehensive decision structure. In what way should they influence the reliability of the basis for decision-making? Put in another way, upon which characteristics can qualitative aspects of knowledge be evaluated and how can these be taken into account in risk and decision analysis?

Finally, how can an evaluation of the quality of knowledge be incorporated in a Bayesian decision framework? When is the Bayesian approach not sufficient to treat uncertainty? In what ways, if all, must the Bayesian approach be developed when new treatments of uncertainty enter the scene?

These and other questions led us to initiate a discussion on the theme “Confronting uncertainties beyond Bayesianism”. The discussion will be spread out to two occasions.

March 12th we discussed the possibilities and limits of Bayesian and alternative decision theories to deal with severe uncertainty.

Invited guests were Nils-Eric Sahlin, Peter Gärdenfors and Rasmus Bååth

May 11th we discussed how to determine and consider confidence in an assessment finding.

Invited guest was Kristie Ebi from University of Washington.

Link to Ebi’s open lecture

Contact Ullrika Sahlin if you are interested to know more

May 5, 2015

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Science of decision making in research – summary and link to presentations

Our fabolous communication staff helped us to document the presentations and put them out on the web. Check this out!

Here is a summary of the workshop

The workshop was held January 13th 2015 and hosted 19 participants from different departments and the CEC. The presentations truly stimulated a discussion on scientific methods to integrate decision problems into research.

Engaging stakeholders in research projects

Anna-Maria Jönsson (The interface between science and stakeholder interactions)talked about the outcome of a study they had made on how stakeholders and researchers perceived the stakeholder interactions and dialogues.

Susanna Bruzell (Integrating stakeholders in adaptation research – a case study of the Swedish forestry sector’s decision-making in a changing climate.) provided examples of the stakeholder dialogues in a larger research environment.

Johanna Alkan-Olsson (What does science say about how and why to interact with stakeholders in research projects) argued for the need to engage stakeholders in research.

Formulating and solving a multi-criteria decision problem

Ullrika Sahlin (The science of Multi-Criteria Decision analysis) gave an introduction to Decision theory, decision making under uncertainty and the formulation of complex decision problems.

Deniz Koca (The Role of Stakeholder Participation and Group Modelling in Developing Decision Support) showed how he in interaction with experts and stakeholders specify the system model upon which a decision analysis will be based.

Nils Cronberg (How we teach decision making in a netbased course in environmental science) presented how they work with teaching students methods to perform evidence based conservation.

Carin Nilsson (Adapting to an uncertain climate – lessons from practice – the CIRCLE-2 findings from Europe) picked some results from a larger study on methods to address and communicate uncertainty in climate adaptation.

Obtaining data from stakeholders

Stina Alriksson (Methods to measure stakeholder’s preferences) showed how she is obtaining data on how stakeholder’s value multiple objectives which can be further used in decision analysis but also to identify groups with common values.

General discussion covered issues like

We concluded that there is a need to build a shared competence and experience of scientific principles to address complex decision problems. We should be better to interact on this type of issues. Knowing the various competences among us on these methods can help knowledge exchange. We need to increase our capacity to utilize existing research projects to work towards managers and policy makers. A competence and resources to integrate decision theory in research and more directly work towards decision makers could be something that makes CEC different from traditional departments at the university.

Integrating decision making into science challenges our view and models of science. A discussion on how to meet the demand for science to provide decision support should be combined with a discussion on scientific integrity.

We should build up a capacity to not only transfer scientific knowledge but to also specify and develop the scientific methodology on doing so in parallel with knowledge transfer. There is e.g. a need for an alternative to evidence-based methods that fits into the type of applications we are working with.

We suggest making some effort to create contact points between research projects working with multi-criteria decision making or that has a goal to communicate uncertainty in their results. A second workshop of a similar kind is desired. Future topics could cover how to explain the development and use of scenarios and communication of uncertainty as part of the scientific method and not something that comes in when the results are there.

i.e. to be continued. / Ullrika

 

February 19, 2015

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Introduction to Bayesian modelling

Introduction to Bayesian Hierarchical Modelling

– a pre-conference hands-on session

Ullrika Sahlin and Paul Caplat, Centre of Environmental and Climate Reserach

Bayesian modelling allows addressing complex problems with flexible model structure arranged in a nested or hierarchical way (hence the name BHM). We will demonstrate Bayesian updating using MCMC sampling in two commonly used open source software. For the sake of simplicity and generality we will introduce BHM using simple examples, increasing complexity (i.e. hierarchical nature) of the models step by step. Bayesian modelling allows addressing complex problems with flexible model structure arranged in a nested or hierarchical way (hence the name BHM).

We hope that the attendants will leave the workshop able to further explore the fun and usefulness of BHM.

Participants were asked to bring their own laptop. It was also possible to attend by looking over the shoulder of someone else.

The modelling will be performed in R with pre-made code that we put up on the conference website prior to the workshop. We recommended installing the programs before the workshop. We are running the examples in two types of software. Which one you choose is up to you, both have their advantages and they look very similar in the R-coding.

The examples we will go through are the following:

Bayesian updating: •The chance to get heads up in a tossing experiment using a Binomial model. •Bayesian linear regression and the influence of priors having little or lots of data.

Hierarchical modelling: •Bayesian linear modelling with a random effect. •Bayesian calibration of a process based model of population dynamics over time. •Bayesian calibration of the population dynamics model considering the error in observations using a Binomial model.

 

February 11, 2015

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We got updated!

This years conference Bayes@Lund is now over. We thank the participants for their interest and dicussions both in the hands-on session and at the presentations. 
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February 11, 2015

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The science of decision making in science

Jan 13th 2015 we had workshop on the science of decision making in research

The purpose of the workshop was to share and discuss scientific methods to integrate decision problems into research. An ambition is to create contact points between BECC/MERGE projects working with multi-criteria decision making and stimulate methodological discussions.
We invited scientists with interest or experience in integrating decision analysis into their research BECC/MERGE associated stakeholders with particular interest in methodological questions
Tuesday 16th 2015, Prefekteriet Ecology building bottom floor
This was the program
09:00 Ullrika Welcome and introduction Anna-Maria Jönsson – The interface between science and stakeholder interactions Ullrika Sahlin – The science of Multi-Criteria Decision analysis Deniz Koca – The Role of Stakeholder Participation and Group Modelling in Developing Decision Support Dicsussion Time for coffee at CEC 10:15 Stina Alriksson – Methods to measure stakeholder’s preferences Susanna Bruzell – Integrating stakeholders in adaptation research – a case study of the Swedish forestry sector’s decision-making in a changing climate. Dicsussion Johanna Alkan-Olsson – What does science say about how and why to interact with stakeholders in research projects Nils Cronberg – How we teach decision making in a netbased course in environmental science Carin Nilsson – Adapting to an uncertain climate – lessons from practice – the CIRCLE-2 findings from Europe Discussion General discussion
December 16, 2014

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Bayes@Lund2015 Feb 10th

bayes_at_lund_logo

Welcome to Bayes@Lund 2015 – a Mini-conference on Bayesian Methods at Lund University, 10th of February, 2015

 

The purpose of this conference is to bring together researchers working with or interested in Bayesian methods. The focus will be on how Bayesian methods are used in research, what advantages Bayesian methods have over classical alternatives, and how the use and teaching of Bayesian methods can be encouraged. As last year the conference will include a number of short contributed talks. New for this year is that there will also be a hands on session which will introduce Bayesian Hierarchical Modelling.

Program

Click here to download the full program, including abstracts of all the talks.

09.15–11.00 Pre-conference hands on session Introduction to Bayesian Hierarchical Modelling. (In room EC1:369)
11.15–12.30 Welcome & Session 1: Foundations, Pedagogy and Pollen
– Bayes from a frequentist point of view, Krzysztof Podgorski, Departement of Statistics, Lund University
– Teaching Bayesian data analysis in psychology, Geoffrey R. Patching, Department of Psychology, Lund University
– Lindley’s paradox, Bengt Ringnér, Mathematical Statistics, Lund University
– Pollen based spatial reconstruction of past land cover, Behnaz Pirzamanbin et al., Mathematical Statistics, Lund University
12.30–13.15 Sandwich Lunch in the lobby (free, but registration is required)
13.15–14.15 Keynote speaker: Mattias Villani, Linköping University – Bayesian model inference – why, what and how?
14.30–15.45 Session 2: Computation, Cells and Socks
– Tiny data, approximate Bayesian Computation and the socks of Karl Broman, Rasmus Bååth, Lund University Cognitive Science
– Data-Cloning ABC for (approximate) maximum likelihood estimation, Umberto Picchini, Mathematical Statistics, Lund University
– Joint cell population identification through Bayesian hierarchical modeling, Kerstin Johnsson, Centre for Mathematical Sciences, Lund University
– Distributing a collapsed sampler for topic models, Måns Magnusson et al., Lindköping Universit
15.45–16.15 Coffee and Cake
16.15–17.30 Session 3: Belief, Money and the Moose Population
– Estimation of local moose population using Bayesian hierarchical modelling, Jonas Wallin, Matematiska vetenskaper, Chalmers
– Performance of Bayesian prediction of treatment differences using a two-factor linear mixed-effects model, Johannes Forkman, SLU
– Bayesian estimation of optimal portfolio, Stepan Mazur, Department of Statistics, Lund University
– How Bayesian belief networks can help save the world, Ullrika Sahlin, Centre for Environmental and Climate Research, Lund University

The post-conference discussion will be held at restaurant Ha Long (Östra Mårtensgatan 19).

For more info, contact

Rasmus Bååth, Lund University Cognitive Science, Department of Philosophy: rasmus.baath [at] lucs.lu.se‎
Ullrika Sahlin, Lund University Centre of Environmental and Climate Research: ullrika.sahlin [at] cec.lu.se

If you want to get notified of other events at Lund University that relates to Bayesian methods consider subscribing to the Bayes@Lund mailing list at http://www.lucs.lu.se/bayes/

Acknowledgement

The organizing comitee of this year’s Bayes@Lund consists of:

  • Ullrika Sahlin at Lund University Centre of Environmental and Climate Research
  • Rasmus Bååth at Lund University Cognitive Science
  • Krzysztof Podgorski at Department of Statistics
  • Paul Caplat at Lund University Centre of Environmental and Climate Research

We are deeply grateful to the Department of Statistics, School of Economics and Management for financial support of this event!

 

December 9, 2014

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